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Highlights
- Vigilant data protection is critical for the success of any merger and acquisition (M&A) given the different priorities of the seller and the buyer.
- The seller values speed of implementation and cost efficiencies while protecting sensitive and non-divestiture-related data, while buyers prioritize control and long-term compliance.
- Establishing trust through guardrails around GenAI, therefore, is imperative. This will unlock unprecedented value while ensuring data privacy and security remain at the forefront of every transaction.
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Banking on Gen AI
Generative artificial intelligence (GenAI) is reshaping the merger and acquisition (M&A) transaction lifecycle.
It is enabling faster and more efficient decision-making, thus ensuring smoother M&A.
Traditionally, the M&A lifecycle has relied heavily on intensive manual cognitive tasks—from target identification and pre-deal due diligence to post-sign-off synergy realization and integration.
Now, GenAI promises to automate all these cognitive tasks. Figure 1 summarizes the GenAI use cases in the M&A value chain.
Figure 1: Gen AI use cases in the M&A value chain
However, amid the promises and excitement it holds, , several instances of GenAI’s unintended ‘whispering’, where the model discloses private or secured information to unauthorized users, has caused concerns. That raises the question: Can GenAI work in M&A, without disclosing secrets?
M&A data privacy concerns
Confidentiality is essential to protect the interests of both the parties involved in an M&A transaction.
Both the seller and the buyer will have different needs and risk appetites when adopting Al solutions for M&A activities. Essential data security and privacy considerations for the buyer and seller will also be different. For sellers, maintaining clear data ownership and ensuring it remains secure throughout the transaction process is paramount. Buyers focus on not just due diligence but also securing data throughout integration, and ensuring compliance with regulations in the long term.
Virtual data rooms (VDRs) are indispensable in M&A transactions, offering secure platforms to manage sensitive information. Buyers gain from cost and time savings, transparency, and streamlined due diligence, enhancing their ability to navigate complex deals. Sellers benefit from the simplicity of the setup, competitive pricing, and efficient data management, ensuring confidentiality and compliance. However, challenges exist: buyers need additional document management work, and they have to grapple with pricing pressures, and technical limitations such as online document viewing. Sellers encounter fewer drawbacks with their primary concern being mitigating data security risks. Despite challenges, VDRs remain crucial for facilitating efficient, secure, and compliant M&A transactions.
As GenAl becomes a critical tool in the M&A value chain, data privacy concerns are increasing for companies managing sensitive financial and operational data. Whether using external service providers or deploying internal enterprise GenAl solutions, maintaining confidentiality is essential for both the parties involved in an M&A transaction.
Seller’s guardrails
For the seller, the top-most priority is to secure business-sensitive and non-divestiture-related data.
To address data privacy and security challenges, sellers and buyers should consider three key dimensions: data, infrastructure, regulatory, and legal (Figure 2).
GenAI data privacy and security for M&A
Sellers typically seek speed, ease of implementation, and cost efficiency. For them, leveraging a GenAI solution from a service provider would be an attractive proposition. In the pre-sign-off phase, GenAI-powered tools can help rapidly prepare and package data for potential buyers. Sellers can create segregated virtual environments for each client, ensuring that proprietary data, such as transaction details, financials, and organizational structures, remains isolated and secured. Techniques such as data siloing, where data is accessed separately by potential buyers, and differential privacy ensure sensitive datasets have limited access. Legal agreements, including multi-tenancy clauses, guarantee that data from one client is not inadvertently shared or accessed by others, especially when the buyer may be a competitor.
In the sign-to-close phase, the seller needs to separate, clean, and migrate data to the target and ensure consistent data classification and security policies are implemented across the dataset. Encryption and data anonymization techniques are also applied to reduce the exposure of critical data points during analysis. Moreover, GenAI cannot trace how data is sourced; hence, it is essential to maintain a data source log to enhance the transparency and traceability of outputs.
Buyer’s guardrails
Buyers tend to have deeper concerns over control, long-term efficiency, and regulatory compliance.
An enterprise GenAI solution in a private, controlled environment offers buyers more robust tools to perform extensive due diligence without exposing proprietary data to third-party vendors. Buyers can build and deploy on-premises models or leverage private cloud environments, ensuring that all sensitive data related to potential or ongoing M&A transactions never leaves the organization's internal network.
In addition to the techniques mentioned above, companies with internal solutions often implement role-based access controls (RBAC), ensuring that only authorized personnel can access the Al models and the data being processed. Moreover, internal governance policies ensure compliance with regulatory standards such as General Data Protection Regulation (GDPR), and Health Insurance Portability and Accountability Act (HIPAA) while giving companies complete ownership and oversight of the GenAI lifecycle.
Extracting value
M&A in the age of GenAI is about unlocking value while safeguarding data privacy.
GenAI’s adoption in the M&A space holds immense promise, streamlining the transaction process and enhancing productivity. M&A deals are inherently complex, involving high-stakes decisions and vast amounts of confidential information. GenAI can significantly improve efficiency in target identification, due diligence, and post-merger integration. With this promise comes new challenges in safeguarding sensitive data. Hence, it is crucial to balance innovation with security. For both sides of the deal, it is essential to implement robust guardrails carefully.
In conclusion, the future of M&A is undoubtedly intertwined with the continued evolution of GenAI. By adopting the proper guardrails, the M&A community can navigate this new landscape, unlocking unprecedented value while ensuring data privacy and security remain at the forefront of every transaction.
